Iterative Refinement for Structural Optimization of De Novo Protein Sequences
- 1University of Texas at Dallas
Abstract
In this paper, we introduce an iterative refinement procedure that substitutes individual amino acid residues in a predicted protein sequence to minimize structural error; substitutions are guided by a weighted composite score of global evaluation metrics including the TM-score, GDT-TS, and RMSD. Starting from an initial sequence prediction produced by a structure-aware graph neural network (GNN) [5], the refinement procedure evaluates candidate amino acid substitutions at each position by folding the modified sequence with ESMFold [9] and accepting substitutions that satisfy both a global structural improvement criterion and a local displacement threshold. Evaluated on a matched sample of 224 proteins, this set of methods reduced mean RMSD from 8.097 [A] to 4.570 [A] relative to the model's initial prediction, a 43.6% reduction, with corresponding reductions of 46.2% relative to ProteinMPNN and 27.2% relative to native ESMFolded sequences; these differences were statistically significant by paired t-test (p < 0.001). Our results show that post-prediction iterative refinement can significantly improve the structural accuracy of de novo protein sequences.
Related
- Flora-YOLO: A lightweight framework for real-time rose grading on edge devices via structural optimization and feature refinement
- Steering sequence generation in protein language models through Iterative Lookback Monte Carlo Sampling
- ProteoEM: probabilistic protein abundance estimation from iterative affinity traces
- Successor representation supports structural learning of syllable sequences
- Multimodal Protein Retrieval via Joint Representation Learning from Sequences and Cryo-EM Density Maps
BibTeX
@misc{gammill2026iterative,
title = {{Iterative Refinement for Structural Optimization of De Novo Protein Sequences}},
author = {C. M. Gammill and L. Tamil},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.27.762198},
url = {https://www.biorxiv.org/content/10.64898/2026.10.27.762198v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Iterative Refinement for Structural Optimization of De Novo Protein Sequences
AU - C. M. Gammill
AU - L. Tamil
PY - 2026
DA - 2026/10/27
PB - bioRxiv
DO - 10.64898/2026.10.27.762198
UR - https://www.biorxiv.org/content/10.64898/2026.10.27.762198v1
AB - In this paper, we introduce an iterative refinement procedure that substitutes individual amino acid residues in a predicted protein sequence to minimize structural error; substitutions are guided by a weighted composite score of global evaluation metrics including the TM-score, GDT-TS, and RMSD. Starting from an initial sequence prediction produced by a structure-aware graph neural network (GNN) [5], the refinement procedure evaluates candidate amino acid substitutions at each position by folding the modified sequence with ESMFold [9] and accepting substitutions that satisfy both a global structural improvement criterion and a local displacement threshold. Evaluated on a matched sample of 224 proteins, this set of methods reduced mean RMSD from 8.097 [A] to 4.570 [A] relative to the model's initial prediction, a 43.6% reduction, with corresponding reductions of 46.2% relative to ProteinMPNN and 27.2% relative to native ESMFolded sequences; these differences were statistically significant by paired t-test (p < 0.001). Our results show that post-prediction iterative refinement can significantly improve the structural accuracy of de novo protein sequences.
ER -